How can safety cases be used to help with frontier AI safety?

Note to readers: we changed our name to the AI Security Institute on 14 February 2025. Read more here.

Safety frameworks have become standard practice amongst frontier AI developers. In them, developers outline key risks, how they’ll measure them and steps to mitigate them. But this is no easy task, particularly when the risks are novel, fast-changing, and hard to pin down.

Over 11 frameworks have now been released, and counting , spurring a wave of research on how to write, implement, and refine them. Two of our latest papers contribute to that conversation. The first provides developers an overview of emerging practices in safety frameworks. The second proposes a method to help implement them: safety cases .

They are broadly useful whenever decisions about safety are being made. At the moment, they are likely most useful for internal company decision-making. In the future, we can imagine safety cases being shared with third parties, and published in some format, much like model cards and capability evaluations are today.

In this blog, we explain what safety cases are and how they can assist AI developers in determining whether an AI system meets the safety thresholds outlined in their safety framework.

Effectively implementing safety frameworks means demonstrating that an AI system is safe. To do that, three things are needed:

Safety cases are a widely used technique that brings all three of these together into a single clear, assessable argument ( Favaro et al., 2023 ; Sujan et al., 2016 ; Bloomfield et al., 2012 ; Inge, 2007 ).